This article will give you tips on how to analyze responses from a Patient survey about Patient Portal Usability. If you want deeper insights and easier workflows, using AI-powered methods is the way to go.
Choosing the right tools for analyzing survey responses
How you analyze Patient survey results depends on the type of data you gathered—some is easy to summarize in a spreadsheet, while other insights require robust AI tools.
Quantitative data: If you’re working with numbers—like how many Patients selected specific options—this fits perfectly into conventional tools like Excel or Google Sheets. These solutions let you quickly count, sort, and graph trends without much setup. For example, 93.4% of surveyed patients reported that their portals were easy to use and 76.1% found them valuable, which you can easily calculate and visualize with basic spreadsheet formulas. [1]
Qualitative data: When you have open-ended survey questions or follow-up conversations, it’s nearly impossible (not to mention exhausting) to read and manually organize them. This type of feedback—stories, frustrations, or ideas—needs AI. Natural language models quickly surface patterns, core ideas, or blind spots that might take ages for a human to spot. AI doesn’t just help—it’s essential for making sense of hundreds or thousands of patient comments.
There are two approaches for tooling when dealing with qualitative responses:
ChatGPT or similar GPT tool for AI analysis
Quick solution: You can export all your open-ended survey replies to a document and copy them into ChatGPT (or similar GPT-based AI models). Chatting about the data this way offers flexibility—you can quickly ask questions, get instant summaries, or dig deeper on any topic.
Downside: The process is clunky and manual. Exporting, copying, and pasting into ChatGPT gets tedious if you have lots of responses. Plus, there’s a risk of losing track of context—or hitting context size limits—if your data set is big. You’ll also miss out on features designed specifically for research data, like filtering, organizing follow-ups, or tracking conversation threads.
All-in-one tool like Specific
Purpose-built platform: Specific was created to bring the power of GPT-based AI into conversational research and survey analysis. It handles both survey creation and analysis—but its magic is in collecting richer responses (with follow-ups) and then instantly breaking down large volumes of qualitative feedback into digestible insights.
Smarter data collection: When gathering responses, Specific’s AI probes with intelligent follow-up questions. This not only boosts completion rates but captures deeper context and clarifies ambiguous answers. If you want to see how this works, here’s a good guide for choosing the best questions for a patient portal usability survey.
Lightning-fast analysis: Once responses start rolling in, Specific uses AI to automatically summarize each question’s replies, identify the main themes, and highlight actionable points instantly—no more spreadsheets or manual coding. The platform even lets you chat with AI about your results, refining questions as you go and getting context-rich insights on demand.
Superior controls: You can filter which data AI analyzes and manage context precisely. All these features are purpose-built for research and survey work, so you’ll spend less time wrestling with imports and exports, and more time understanding what patients actually think about portal usability.
If you haven’t started your survey yet, you can use the AI survey generator for Patient Portal Usability to quickly set up a professionally structured survey with tailored follow-ups.
Useful prompts you can use to analyze Patient survey responses about Patient Portal Usability
The right prompts can supercharge your analysis—getting you straight to the core ideas and tough questions buried in your data. Here are some you’ll find useful, whether you’re using ChatGPT, Specific, or any other GPT-based tool.
Prompt for core ideas: This is the secret weapon for mapping high-volume qualitative answers into crisp, actionable insights. It’s also the backbone of how Specific distills patient survey responses.
Your task is to extract core ideas in bold (4-5 words per core idea) + up to 2 sentence long explainer.
Output requirements:
- Avoid unnecessary details
- Specify how many people mentioned specific core idea (use numbers, not words), most mentioned on top
- no suggestions
- no indications
Example output:
1. **Core idea text:** explainer text
2. **Core idea text:** explainer text
3. **Core idea text:** explainer text
AI always performs better when you tell it more about your survey, the respondents, and your goal. For example, providing this context before your main prompt will drive better, more nuanced analysis:
This survey collects responses from Patients about their experience using digital healthcare portals at their clinic in 2024. The goal is to understand which features they find most intuitive, what challenges they face, and opportunities for improvement. Please focus on these priorities.
Prompt for follow-up: Want detail on a specific patient concern or trend? Simply ask:
Tell me more about "managing appointments online" (core idea)
This works for any topic or finding you want to drill into. For example, since 60% of patients report higher satisfaction when managing appointments online [7], you’ll want to unpack feedback on this area for deeper product recommendations.
Prompt for specific topic: Validate a hunch or check for blind spots by asking:
Did anyone talk about test result access? Include quotes.
Prompt for personas: Segment your patients into groups for more personalized follow-up:
Based on the survey responses, identify and describe a list of distinct personas—similar to how "personas" are used in product management. For each persona, summarize their key characteristics, motivations, goals, and any relevant quotes or patterns observed in the conversations.
Prompt for pain points and challenges: Pinpoint what users struggle with—valuable for UX improvements or new feature ideas. For example, even though 90% of users find portals convenient, there are still opportunities to enhance usability, as cross-country studies show usability ranges from good to fair [5].[4]
Analyze the survey responses and list the most common pain points, frustrations, or challenges mentioned. Summarize each, and note any patterns or frequency of occurrence.
Prompt for sentiment analysis: Get an emotional pulse on the patient experience:
Assess the overall sentiment expressed in the survey responses (e.g., positive, negative, neutral). Highlight key phrases or feedback that contribute to each sentiment category.
Prompt for suggestions & ideas: Harvest your patients’ creative ideas and requests:
Identify and list all suggestions, ideas, or requests provided by survey participants. Organize them by topic or frequency, and include direct quotes where relevant.
Prompt for unmet needs & opportunities: Find hidden opportunities behind what isn’t being served:
Examine the survey responses to uncover any unmet needs, gaps, or opportunities for improvement as highlighted by respondents.
If you want even more inspiration for survey design, check out how to create a great patient survey about portal usability or use the survey generator for patient portal usability feedback.
How Specific analyzes open-ended survey questions about patient portals
Open-ended questions with or without follow-ups: Specific automatically generates an AI-powered summary for every open-ended question—capturing both the primary answer and any dynamic follow-ups the AI asked. This means the nuances behind “Why do you use the portal?” or “Can you describe a problem you faced?” aren’t lost in translation. You get a readable, actionable summary for each cluster of responses.
Choices with follow-ups: If your survey used multiple-choice questions with follow-ups (“What feature do you use most?” with an extra probe for each selection), Specific breaks down summaries according to each choice, giving you instant clarity on the “why” behind every option.
NPS: For Net Promoter Score questions, Specific splits the follow-up responses between detractors, passives, and promoters. Each has its own summary—so you can see specifically what makes promoters rave and what sticks out for detractors (or keeps passives on the fence). For best results, try the NPS survey builder for patient portal usability if you want this baked-in logic immediately.
You can do much of this with ChatGPT, but organizing and tagging open-ended follow-up responses is much more manual, and it’s easy to miss patterns across linked questions.
If follow-up depth matters to you, learn how automatic AI follow-up questions in Specific turn flat data into layered, conversational insight.
How to tackle challenges with context limits in AI survey analysis
AI models (even the latest) have a “context window”—which means they can only process a set amount of data at once. If your patient survey generated hundreds of extended conversations, not all may fit into the AI’s brain at once, creating blind spots or cutting off results.
To solve this, you have two best-in-class strategies, both available by default in Specific:
Filtering: Only analyze conversations where respondents answered specific questions or made key choices. This narrows data to what’s actually relevant, easily staying beneath AI context limits, and surfacing the most critical feedback.
Cropping: Instead of sending every question to the AI, you can select the most important ones (for example, just open-ended or complaint-based questions). By cropping unnecessary data out, you ensure more complete conversations from more respondents can be included in a single AI-powered analysis.
The same approaches work in ChatGPT or other models, but you’ll do more prep work—it pays to use a tool that understands your survey structure out of the box.
Collaborative features for analyzing Patient survey responses
Collaboration is hard when everyone’s lost in different spreadsheets or ChatGPT threads. Especially with Patient Portal Usability surveys, teams need to quickly share, discuss, and prioritize findings together—without email overload or missed comments.
Specific is built for teamwork: You can analyze Patient survey data by chatting with AI—no technical skills needed. Imagine spinning up multiple chats, each with focused filters or different questions, and instantly seeing who started each analysis thread. If two product leads want to explore usability while a third investigates NPS comments, no one’s stepping on each other’s toes.
Every message shows who said what. In every research chat, you instantly see sender avatars—making it effortless to track conversations, attribute findings to the right teammates, and keep everyone looped in.
Filters unlock collaborative workflows. If you want only conversations from patients who sent secure messages (which 60% do [2]), you can filter and assign the chat, or even annotate follow-ups right in the analysis window. Your entire team can share the load, driving toward actionable insights faster.
Want to create or edit better surveys collaboratively? Try the AI survey editor, where you can brainstorm and refine survey questions in real time.
Create your Patient survey about Patient Portal Usability now
Get rich, real-time Patient feedback, analyze every conversation with AI, and unlock insights that improve portal usability—start your survey today for instant results.